Abstract:
OBJECTIVES:The goal of the present study was to classify the most common types of plain radiographs using a neural network and to validate the network's performance on internal and external data. Such a network could help improve various radiological workflows. METHODS:All radiographs from the year 2017 (n = 71,274) acquired at our institution were retrieved from the PACS. The 30 largest categories (n = 58,219, 81.7% of all radiographs performed in 2017) were used to develop and validate a neural network (MobileNet v1.0) using transfer learning. Image categories were extracted from DICOM metadata (study and image description) and mapped to the WHO manual of diagnostic imaging. As an independent, external validation set, we used images from other institutions that had been stored in our PACS (n = 5324). RESULTS:In the internal validation, the overall accuracy of the model was 90.3% (95%CI: 89.2-91.3%), whereas, for the external validation set, the overall accuracy was 94.0% (95%CI: 93.3-94.6%). CONCLUSIONS:Using data from one single institution, we were able to classify the most common categories of radiographs with a neural network. The network showed good generalizability on the external validation set and could be used to automatically organize a PACS, preselect radiographs so that they can be routed to more specialized networks for abnormality detection or help with other parts of the radiological workflow (e.g., automated hanging protocols; check if ordered image and performed image are the same). The final AI algorithm is publicly available for evaluation and extension. KEY POINTS:• Data from one single institution can be used to train a neural network for the correct detection of the 30 most common categories of plain radiographs. • The trained model achieved a high accuracy for the majority of categories and showed good generalizability to images from other institutions. • The neural network is made publicly available and can be used to automatically organize a PACS or to preselect radiographs so that they can be routed to more specialized neural networks for abnormality detection.
journal_name
Eur Radioljournal_title
European radiologyauthors
Dratsch T,Korenkov M,Zopfs D,Brodehl S,Baessler B,Giese D,Brinkmann S,Maintz D,Pinto Dos Santos Ddoi
10.1007/s00330-020-07241-6subject
Has Abstractpub_date
2020-09-28 00:00:00eissn
0938-7994issn
1432-1084pii
10.1007/s00330-020-07241-6pub_type
杂志文章abstract:OBJECTIVES:To (a) determine the diagnostic validity of axillary ultrasound (AUS) in pT1 tumours and whether fine-needle aspiration (FNA) improves its diagnostic performance, and (b) determine the negative predictive value (NPV) of AUS in a simulation environment (cutoff: two lymph nodes with macrometastases) in patient...
journal_title:European radiology
pub_type: 杂志文章,多中心研究
doi:10.1007/s00330-015-3901-2
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abstract:OBJECTIVE:To determine the possible influence of segmentation margin on each step (feature reproducibility, selection, and classification) of the machine learning (ML)-based high-dimensional quantitative computed tomography (CT) texture analysis (qCT-TA) of renal clear cell carcinomas (RcCCs). MATERIALS AND METHODS:Fo...
journal_title:European radiology
pub_type: 杂志文章
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journal_title:European radiology
pub_type: 杂志文章
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journal_title:European radiology
pub_type: 信件
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journal_title:European radiology
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pub_type: 杂志文章
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pub_type: 杂志文章
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pub_type: 临床试验,杂志文章
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pub_type: 杂志文章
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pub_type: 杂志文章,随机对照试验
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更新日期:2007-11-01 00:00:00
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pub_type: 杂志文章
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更新日期:2013-07-01 00:00:00
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journal_title:European radiology
pub_type: 杂志文章
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